Arachnid

import bisect

class NFA(object):
  EPSILON = object()
  ANY = object()
  
  def __init__(self, start_state):
    self.transitions = {}
    self.final_states = set()
    self._start_state = start_state

lucasfais

Sublime Text 2 – Useful Shortcuts (Mac OS X)

General

⌘T	go to file
⌘⌃P	go to project
⌘R	go to methods
⌃G	go to line
⌘KB	toggle side bar
⌘⇧P	command prompt

feng92f

王
江
周
胡
刘
李
吴
毛
温
习

1wErt3r

;SMBDIS.ASM - A COMPREHENSIVE SUPER MARIO BROS. DISASSEMBLY
;by doppelganger ([email protected])

;This file is provided for your own use as-is.  It will require the character rom data
;and an iNES file header to get it to work.

;There are so many people I have to thank for this, that taking all the credit for
;myself would be an unforgivable act of arrogance. Without their help this would
;probably not be possible.  So I thank all the peeps in the nesdev scene whose insight into
;the 6502 and the NES helped me learn how it works (you guys know who you are, there's no 

shlomibabluki

# coding=UTF-8
from __future__ import division
import re

# This is a naive text summarization algorithm
# Created by Shlomi Babluki
# April, 2013


class SummaryTool(object):

shlomibabluki

# coding=UTF-8
import nltk
from nltk.corpus import brown

# This is a fast and simple noun phrase extractor (based on NLTK)
# Feel free to use it, just keep a link back to this post
# http://thetokenizer.com/2013/05/09/efficient-way-to-extract-the-main-topics-of-a-sentence/
# Create by Shlomi Babluki
# May, 2013

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

fperez

syllog1sm

"""A simple implementation of a greedy transition-based parser. Released under BSD license."""
from os import path
import os
import sys
from collections import defaultdict
import random
import time
import pickle

SHIFT = 0; RIGHT = 1; LEFT = 2;

jeffaudi

import pytumblr
import os
import code
import oauth2 as oauth
from pprint import pprint
import json
import urllib
import codecs

# Number of likes to fetch in one request